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이어폰 및 헤드폰의 정자기장이 인공심장 박동기 및 이식형 제세동기에 미치는 영향
정재원,최수범,박지수,김덕원,Chung, J.W.,Choi, S.B.,Park, J.S.,Kim, D.W. 대한의용생체공학회 2015 의공학회지 Vol.36 No.1
In this study, we evaluated the effects of static magnetic fields of earphones and headphones on pacemakers and implantable cardioverter defibrillators(ICDs). Five pacemakers and three ICDs were subjected to in-vitro test with three headphones which were an in-ear earphone, clip-on headphone, and closed-back headphone. Each implantable device was placed in close proximity(within 3 mm) to the ear-pad of each of the earphone and headphones for 3 min. As a result, no effects were observed on the pacemakers for the earphone and headphones during the test, but an effect was observed on one ICD for the clip-on and closed-back headphone during the test. When the ICD was placed in close proximity to the headphones, the ICD temporarily suspended functions of tachyarrhythmia detection and therapy. The effect was not observed in this study when the headphones were at least 2 cm from the ICD. Based on these findings, patients with ICDs should be advised to keep earphones and headphones at least 2 cm apart from their ICDs.
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코로나19로 인한 공과대학 교수자의 온라인 수업 경험 탐색
정재원,허정은,박효원 한국공학교육학회 2020 공학교육연구 Vol.23 No.6
This study was conducted to understand the experiences of engineering college professors teaching classes online due to COVID-19. Instructional strategies were proposed based on these results. This study qualitatively analyzed interviews of four engineering college professors who switched from teaching classes offline to teaching them online in the first semester of 2020. The results showed that the professors had difficulties in creating video content, interacting with students, and conducting online assessments. The results also showed that the professors had difficulties in switching to online teaching. This study was significant in that it produced empirical data about online teaching based on the experiences of engineering college professors teaching classes online.
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Return Volatilities of the Korea Treasury Bond in Financial Markets
정재원,김경식 한국물리학회 2012 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.60 No.4
We investigate the long memory property in the volatility of the Korean futures market. For our model, we analyze the high-frequency data of the KTB503 by applying the FIGARCH model. From the result of our analysis, we conclude that the volatility of the KTB503 exhibits the feature of the long memory. In particular, this long memory feature is compared to those used in other studies.
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강우사상의 지속기간별 분포 특성을 고려한 일강우 모의 기법 개발
정재원,김수전,김형수 한국수자원학회 2019 한국수자원학회논문집 Vol.52 No.2
When simulating the daily rainfall amount by existing Markov Chain model, it is general to simulate the rainfall occurrence and to estimate the rainfall amount randomly from the distribution which is similar to the daily rainfall distribution characteristic using Monte Carlo simulation. At this time, there is a limitation that the characteristics of rainfall intensity and distribution by time according to the rainfall duration are not reflected in the results. In this study, 1-day, 2-day, 3-day, 4-day rainfall event are classified, and the rainfall amount is estimated by rainfall duration. In other words, the distributions of the total amount of rainfall event by the duration are set using the Kernel Density Estimation (KDE), the daily rainfall in each day are estimated from the distribution of each duration. Total rainfall amount determined for each event are divided into each daily rainfall considering the type of daily distribution of the rainfall event which has most similar rainfall amount of the observed rainfall using the k-Nearest Neighbor algorithm (KNN). This study is to develop the limitation of the existing rainfall estimation method, and it is expected that this results can use for the future rainfall estimation and as the primary data in water resource design. 기존의 Markov Chain 모형으로 일강우량 모의시에 강우의 발생여부를 모의하고 강우일의 강우량은 Monte Carlo 시뮬레이션을 통해 일강우 분포 특성에 맞는 분포형에서 랜덤으로 강우량을 추정하는 것이 일반적이다. 이때 강우 지속기간에 따른 강도 및 강우의 시간별 분포 등의 강우 사상의 특성을 반영할 수 없다는 한계가 있다. 본 연구에서는 이를 개선하기 위해 강우 사상을 1일 지속강우, 2일 지속강우, 3일 지속강우, 4일이상 지속강우로 구분하여 강우의 지속기간에 따라 강우량을 추정하였다. 즉 강우 사상의 강우 지속일별로 총강우량의 분포형을 비매개변수 추정이 가능한 핵밀도추정(Kernel Density Estimation, KDE)를 적용하여 각각 추정하였고, 강우가 지속될 경우에 지속일별로 해당하는 분포형에서 강우량을 구하였다. 각 강우사상에 대해 추정된 총 강우량은 k-최근접 이웃 알고리즘(k-Nearest Neighbor algorithm, KNN)을 통해 관측 강우자료에서 가장 유사한 강우량을 가지는 강우사상의 강우량 일분포 형태에 따라 각 일강우량으로 분배하였다. 본 연구는 기존의 강우량 추정 방법의 한계점을 개선하고자 하였으며, 연구 결과는 미래 강우에 대한 예측에도 활용될 수 있으며 수자원 설계에 있어서 기초자료로 활용될 수 있을 것으로 기대된다.
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